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Unpinning and elimination of spiral waves: A machine learning-based method

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  • Hu, Yi-peng
  • Ding, Qian-ming
  • Wu, Yong
  • Yu, Dong
  • Li, Tian-yu
  • Jia, Ya

Abstract

Spiral wave is a phenomenon widely observed in excitable media. Defects in cardiac tissue can induce the pinning of spiral waves, leading to arrhythmias. Although electrical defibrillation can restore normal rhythm, it demands high energy and inflicts substantial tissue damage. Optogenetics offers a low-energy alternative, yet the optical energy deposited in tissue inevitably converts to heat, which can undermine efficacy and introduce new safety concerns. This letter proposes a novel strategy based on the Dynamic Learning Synchronization method (DLS-ADMM), utilizing the background of optogenetics to effectively unpin the spiral wave through adaptive light intensity. Our method successfully unpinned and eliminated spiral waves by adaptively adjustment of the light intensity in different defects. Compared with the conventional constant-illumination approach, spiral waves can be unpinned and eliminated by DLS-ADMM at lower light intensity, enabling the low-energy control. This work provides an algorithmic framework and theoretical basis for developing efficient, low-energy defibrillation techniques.

Suggested Citation

  • Hu, Yi-peng & Ding, Qian-ming & Wu, Yong & Yu, Dong & Li, Tian-yu & Jia, Ya, 2026. "Unpinning and elimination of spiral waves: A machine learning-based method," Chaos, Solitons & Fractals, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:chsofr:v:204:y:2026:i:c:s0960077925017783
    DOI: 10.1016/j.chaos.2025.117764
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    References listed on IDEAS

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